RT ANALYSIS

####THIS CHUNK WILL NEED TO BE ADJUSTED TO READ FROM GITHUB DATA###
#THIS CHUNK MAKES MORE SENSE TO MOVE AFTER FUNCTIONS ARE GENERATED
#IT IS LOCATED HERE FOR VISIBILITY ONLY, ONCE MOVED TO GITHUB, RELOCATE TO A MORE LOGICAL
#LOCATION IN THE CODE AS DESIRED
getwd()

## [1] "C:/Users/jeffb/Desktop/Life/personal-projects/COVID"

#read in full DSHS data with county and TSA information
full.dshs<-read.csv("combined-datasets/county.csv")#change path as needed
tsa.dshs<-read.csv("combined-datasets/tsa.csv")#change path as needed

#rt.data.clean function will clean data, dropping unwanted variables
rt.data.clean<-function(covid.data)#note: mydata will be either full.dshs or tsa.dshs data
  {

library(dplyr)
library(tidyr)
library(reshape2)
  str(covid.data)
  #keep variables from this list if they are in the data set
      #(county, date, population, TSA_Name, cases_daily, Population_DSHS, Population_Census)
  covid.data<-covid.data[,names(covid.data)%in% 
                           c("County", #name of texas county 
                             "Date", #date of data recorded
                             "TSA_Name", #name of texas TSA group for county
                             "Cases_Daily", #daily new cases
                             "Population_DSHS", #population for county per DSHS data
                             "Population_Census")] #population for county per Census estimates
                                                  #Note: Census data is from 2010, these values are projections 
                                                        #from that Census as provided by the Census Bureau
  #change Date variable to date format
  covid.data$Date<-as.Date(covid.data$Date)
  #change cases_daily to numeric - if not already numeric
  covid.data$Cases_Daily<-as.numeric(covid.data$Cases_Daily)
  return(covid.data)
}

#separate county, TSA and State data into separate Data frames
rt.data.organize<-function(mycounty, mytsa){
  library(dplyr)
  library(tidyr)
  #call the rt.data.clean function on mydata
  cleaned.county<-rt.data.clean(mycounty)
  cleaned.tsa<-rt.data.clean(mytsa)
  
  #Drop TSA_Name from county data frame
  county.df<-cleaned.county%>%dplyr::select(-TSA_Name)
  
  #create TSA.df, use rt.data.clean function
  TSA.df<-rt.data.clean(mytsa)
  #create state.df
  #at the state level we want daily new cases, and population variables from DSHS and Census data
  state.temp<-cleaned.tsa%>%
    group_by(Date)%>%
    mutate(state_cases_daily=sum(Cases_Daily, na.rm=TRUE))%>%#generate variable of state daily cases
    mutate(state_pop_DSHS=sum(Population_DSHS, na.rm=TRUE))%>%#generate state population_DSHS variable
    mutate(state_pop_Census=sum(Population_Census, na.rm=TRUE))%>%#generate state population_Census variable
    dplyr::select(Date,state_cases_daily, state_pop_DSHS, state_pop_Census)%>%#select only variables of interest
    distinct()#keep only distinct rows (this will drop repeated rows, now that we have only state level vars)
  state.df<-data.frame(Date=state.temp$Date, 
                       Cases_Daily=state.temp$state_cases_daily, 
                       Population_DSHS=state.temp$state_pop_DSHS, 
                       Population_Census=state.temp$state_pop_Census)
  #return a list of the three dataframes generated
 return(list(county=county.df, TSA=TSA.df, state=state.df))
}

rt.df.extraction<-function(Rt.estimate.output){

   #first convert named vector back to dataframe
  #reorder and rename columns from the R0.estimate output
  rt.df<-setNames(stack(Rt.estimate.output$estimates$TD$R)[2:1], c('Date', 'Rt'))
 
  #extract the upper and lower limits of the Rt 95% confidence intervals, these will be lists
  CI.lower.list<-Rt.estimate.output$estimates$TD$conf.int$lower
  CI.upper.list<-Rt.estimate.output$estimates$TD$conf.int$upper

  #use unlist function to format as vector
  CI.lower <- unlist(CI.lower.list, recursive = TRUE, use.names = TRUE)
  CI.upper <- unlist(CI.upper.list, recursive = TRUE, use.names = TRUE)

  #add the upper and lower bounds to the dataframe
  rt.df$lower<-CI.lower
  rt.df$upper<-CI.upper
  
  return(rt.df)
}

)

#Function to generate Rt estimates, need to read in data frame and population size
covid.rt<-function(mydata){
library(R0)
  #change na values to 0
  mydata<-mydata%>%replace(is.na(.),0)
  
  #create a vector of new cases
  mydata.new<-pull(mydata, Cases_Daily)
  #create a vector of dates
  date.vector<-pull(mydata, Date)
  #create a named numerical vector using the date.vector as names of new cases
    #Note: this is needed to run R0 package function estimate.R()
  names(mydata.new)<-c(date.vector)
  
  #create the generation time 
  ##### NOTE: THIS MAY NEED MODIFICATION ONCE MORE INFORMATIONIS AVAILABLE ####
  gen.time<-generation.time("lognormal", c(4.0, 2.9))#verify these values and distribution/update as needed
  #gen.time<-generation.time("gamma", c(3,1.5))
  
  #get DSHS population and census population
  pop.DSHS<-mydata$Population_DSHS[1]
  pop.census<-mydata$Population_Census[1]
  
  #Adjust Dataframe to run from 3/9/20 to current date
  #Find the number of days to run the rt estimate
  min.date<-min(mydata$Date)
  max.date<-max(mydata$Date)
  i<-which(mydata$Date=="2020-03-09")
  total.days<-difftime(max.date, min.date, units="days")
  total.days<-as.integer(total.days)
  
  #reduce the vector to be rows from March 9 to current date
  mydata.newest<-mydata.new[i:total.days]
  
  #run estimate.R() using gen.time, total.days, pop.DSHS
  rt.DSHS<-estimate.R(mydata.newest, 
                      gen.time, 
                      #begin=as.integer(1), 
                      #end=total.days, 
                      methods=c("TD"), 
                      pop.size=pop.DSHS,
                      nsim=1000)
  
  #run estimate.R() using gen.time, total.days, pop.Census
  rt.Census<-estimate.R(mydata.newest, 
                      gen.time, 
                      methods=c("TD"), 
                      pop.size=pop.Census,
                      nsim=1000)
  
  # use rt extraction function to get rt estimates, upper and lower bounds in data frame for both rt.DSHS and rt.Census
    rt.DSHS.df<-rt.df.extraction(rt.DSHS)
    rt.Census.df<-rt.df.extraction(rt.Census)
  
  
  #return list of dataframes
  return(list(rt.DSHS=rt.DSHS.df, rt.Census=rt.Census.df))
}

# TODO

# TODO: fix incid should be positive and non-missing
#apply the covid.rt function to the TSA.daily data by TSA region
# TSA.daily%>%group_by(TSA_Name)%>%covid.rt() 

# TSA.daily%>%group_by(TSA_Name)%>%group_map(~covid.rt)

# TSA_complete = na.omit(TSA.daily)

# TSA_RT = nlme::gapply(TSA.daily, FUN = covid.rt, groups = TSA.daily$TSA_Name)
# 
# TSA_list = rep(NA, length = length(unique(TSA.daily$TSA_Name)))
# for (TSA in TSA.daily$TSA_Name) { 
#   out = covid.rt(subset(TSA.daily, TSA_Name == TSA))
#   TSA_RT = append(test, out)
#   }

#run covid.rt function on state level data
state.rt.list<-covid.rt(state.daily)

## Warning: package 'R0' was built under R version 3.6.3

## Loading required package: MASS

## 
## Attaching package: 'MASS'

## The following object is masked from 'package:dplyr':
## 
##     select

## Warning in est.R0.TD(epid = c(`2020-03-09` = 7, `2020-03-10` = 3, `2020-03-11` =
## 3, : Simulations may take several minutes.

## Warning in est.R0.TD(epid = c(`2020-03-09` = 7, `2020-03-10` = 3, `2020-03-11` =
## 3, : Using initial incidence as initial number of cases.

## Warning in est.R0.TD(epid = c(`2020-03-09` = 7, `2020-03-10` = 3, `2020-03-11` =
## 3, : Simulations may take several minutes.

## Warning in est.R0.TD(epid = c(`2020-03-09` = 7, `2020-03-10` = 3, `2020-03-11` =
## 3, : Using initial incidence as initial number of cases.

#extract rt estimates based on DSHS population estimates
state.rt.DSHSpop<-state.rt.list$rt.DSHS
#extract rt estimates based on Censuspopulation estimates
state.rt.Censuspop<-state.rt.list$rt.Census

write.csv(state.rt.DSHSpop, file="statistical-output/rt/state_rt.csv", row.names=FALSE)

TIME SERIES

#ts.data.clean function will clean data, dropping unwanted variables
#note: mydata will be either full.dshs or tsa.dshs data
ts.data.clean<-function(covid.data) {
  
  library(dplyr)
  library(tidyr)
  library(reshape2)
  str(covid.data)
  #keep variables from this list if they are in the data set
      #(county, date, population, TSA_Name, Cases_Cumulative, Population_DSHS, Population_Census)
  covid.data<-covid.data[,names(covid.data)%in% 
                           c("County", #name of texas county 
                             "Date", #date of data recorded
                             "TSA_Name", #name of texas TSA group for county
                             "Metro_Area", #indicateor of metro/nonmetro county
                             "Cases_Cumulative", #daily new cases
                             "Population_DSHS", #population for county per DSHS data
                             "Population_Census")] #population for county per Census estimates
                                                  #Note: Census data is from 2010, these values are projections 
                                                        #from that Census as provided by the Census Bureau
  #change Date variable to date format
  covid.data$Date<-as.Date(covid.data$Date)
  #change Cases_Cumulative to numeric - if not already numeric
  covid.data$Cases_Cumulative<-as.numeric(covid.data$Cases_Cumulative)
  #change any Cases_Cumulative below zero to zero
  covid.data<-covid.data%>%mutate(Cases_Cumulative=ifelse(Cases_Cumulative<0, 0, Cases_Cumulative))
  return(covid.data)
  
}

#separate county, metro, TSA and State data into separate Data frames
ts.data.organize<-function(mycounty, mytsa){
  library(dplyr)
  library(tidyr)
  
  ### COUNTY ###
  
  #call the ts.data.clean function on mydata
  cleaned.county<-ts.data.clean(mycounty)
  
  # drop NA dates
  cleaned.county<-subset(cleaned.county, !is.na(Date) & Date>=as.Date('2020-03-04'))

  #Drop TSA_Name from county data frame
  county.df<-cleaned.county%>%dplyr::select(-TSA_Name, -Metro_Area)
  county.df$Level = 'County'
  colnames(county.df)[1] = 'Level_Name'
  ### METRO ###

  #create metro.df by mutating by grouping by Metro
  metro.temp<-cleaned.county%>%
    group_by(Metro_Area,Date)%>%
    mutate(metro_Cases_Cumulative=sum(Cases_Cumulative, na.rm=TRUE))%>%#generate variable of state daily cases
    mutate(metro_pop_DSHS=sum(Population_DSHS, na.rm=TRUE))%>%#generate state population_DSHS variable
    mutate(metro_pop_Census=sum(Population_Census, na.rm=TRUE))%>%#generate state population_Census variable
    dplyr::select(Date,Metro_Area, metro_Cases_Cumulative, metro_pop_DSHS, metro_pop_Census)%>%#select only variables of interest
    distinct()#keep only distinct rows (this will drop repeated rows, now that we have only state level va
    
  metro.df<-data.frame(Date=metro.temp$Date,
                       Level = 'metro',
                       Level_Name=metro.temp$Metro_Area,
                       Cases_Cumulative=metro.temp$metro_Cases_Cumulative,
                       Population_DSHS=metro.temp$metro_pop_DSHS,
                       Population_Census=metro.temp$metro_pop_Census
                       )
  
    
  # drop NA dates
  metro.df<-subset(metro.df, !is.na(Date) & Date>=as.Date('2020-03-04'))
  
  ### TSA ###
  TSA.df<-ts.data.clean(mytsa)
  TSA.df<-subset(TSA.df, Date>=as.Date('2020-03-04'))
  
  TSA.df$Level = 'TSA'
  colnames(TSA.df)[2] = 'Level_Name'
  ### STATE ###
  
  #create state.df
  #at the state level we want daily new cases, and population variables from DSHS and Census data
  state.temp<-TSA.df%>%
    group_by(Date)%>%
    mutate(state_Cases_Cumulative=sum(Cases_Cumulative, na.rm=TRUE))%>%#generate variable of state daily cases
    mutate(state_pop_DSHS=sum(Population_DSHS, na.rm=TRUE))%>%#generate state population_DSHS variable
    mutate(state_pop_Census=sum(Population_Census, na.rm=TRUE))%>%#generate state population_Census variable
    dplyr::select(Date,state_Cases_Cumulative, state_pop_DSHS, state_pop_Census)%>%#select only variables of interest
    distinct()#keep only distinct rows (this will drop repeated rows, now that we have only state level vars)
    
  state.df<-data.frame(Date=state.temp$Date,
                       Level = 'State',
                       Level_Name = 'Texas',
                       Cases_Cumulative=state.temp$state_Cases_Cumulative, 
                       Population_DSHS=state.temp$state_pop_DSHS, 
                       Population_Census=state.temp$state_pop_Census
                       )
  
  state.df<- subset(state.df, Date>=as.Date('2020-03-04'))
  
  #return a list of the three dataframes generated
  return(list(county=county.df, TSA=TSA.df, state=state.df, metro=metro.df))
}

library(forecast)

## Warning: package 'forecast' was built under R version 3.6.3

## Registered S3 method overwritten by 'xts':
##   method     from
##   as.zoo.xts zoo

## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo

library(ggplot2) 

library(astsa)

## Warning: package 'astsa' was built under R version 3.6.3

## 
## Attaching package: 'astsa'

## The following object is masked from 'package:forecast':
## 
##     gas

library(forecast)
library(zoo)

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

library(ggplot2)
library(fpp2)

## Warning: package 'fpp2' was built under R version 3.6.3

## Loading required package: fma

## Warning: package 'fma' was built under R version 3.6.3

## 
## Attaching package: 'fma'

## The following objects are masked from 'package:astsa':
## 
##     chicken, sales

## The following objects are masked from 'package:MASS':
## 
##     cement, housing, petrol

## Loading required package: expsmooth

## Warning: package 'expsmooth' was built under R version 3.6.3

## 
## Attaching package: 'fpp2'

## The following object is masked from 'package:astsa':
## 
##     oil

library(gridExtra)

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:dplyr':
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covid.arima.forecast<-function(mydata)
{
  maxdate<-max(mydata$Date)
  mindate <-min(mydata$Date)
  prediction.period<-10 #this can be changed as desired
  
  if(max(mydata$Cases_Cumulative>=5, na.rm = TRUE))
  {
    
    #for the time series, you need a start day, which is the year, and the day number - 
    #we need to double check how this works exactly
    my.timeseries<-ts(mydata$Cases_Cumulative)
    
    #get a fit model using the best ARIMA from auto.arima
    arima.fit<-forecast::auto.arima(my.timeseries)
    
    #use predict() on the fit model
    # prediction.period is how many more days to forecast
    # 10 day forecast
    arima.forecast<-forecast::forecast(arima.fit, h = prediction.period) 
    
    #auto correlation function
    # diagnostics
    acf<-ggAcf(my.timeseries, lag.max=30)
    pacf<-ggPacf(my.timeseries, lag.max=30)
    grid.arrange(acf, pacf, nrow=2)
    ggsave(paste0('statistical-output/time-series/diagnostics/', mydata$Level[1],'/', mydata$Level_Name[1], '.png'))
     
    #return a dataframe of the arima model(cumulative cases by date) 
    #and forecasted values ***NOT SURE HOW THIS WILL WORK***
    arima.out<-data.frame(Date = seq(mindate, maxdate + 10, by = 'days'),
                          Cases.Cumulative = c(my.timeseries, arima.forecast[['mean']]),
                          CI.Lower = c(rep(NA, times = length(my.timeseries)), arima.forecast[['lower']][, 2]),
                          CI.Upper = c(rep(NA, times = length(my.timeseries)), arima.forecast[['upper']][, 2]))

    } else {
      
    arima.out<-data.frame(Date = seq(mindate, maxdate + 10, by = 'days'),
                          Cases.Cumulative = c(mydata$Cases_Cumulative, rep(NA, times = 10)),
                          CI.Lower = rep(NA, times = length(mydata$Date) + 10),
                          CI.Upper =  rep(NA, times = length(mydata$Date) + 10)
                          )
  }
  return(arima.out)
}

#get county forecasts using covid.arima.forecast function

#load nlme package
library(nlme)

## 
## Attaching package: 'nlme'

## The following object is masked from 'package:forecast':
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##     getResponse

## The following object is masked from 'package:dplyr':
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##     collapse

#apply the covid.arima.forecast function to the county.cumulative dataframe by county
#note that the output will be a list of dataframes
county.arima.output<-nlme::gapply(county.cumulative, FUN = covid.arima.forecast, groups=county.cumulative$Level_Name)

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#load data.table package
library(data.table)

## 
## Attaching package: 'data.table'

## The following objects are masked from 'package:reshape2':
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## The following objects are masked from 'package:dplyr':
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county.arima.df<-data.table::rbindlist(county.arima.output, idcol=TRUE)

colnames(county.arima.df)[1] = 'Level_Name'
county.arima.df$Level = 'County'

#get metro forecasts using covid.arima.forecast function
#load nlme package
library(nlme)
#apply the covid.arima.forecast function to the metro.cumulative dataframe by metro
#note that the output will be a list of dataframes
metro.arima.output<-nlme::gapply(metro.cumulative, FUN = covid.arima.forecast, groups=metro.cumulative$Level_Name)

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#load data.table package
library(data.table)
metro.arima.df<-data.table::rbindlist(metro.arima.output, idcol=TRUE)

colnames(metro.arima.df)[1] = 'Level_Name'
metro.arima.df$Level = 'metro'

#ge#load nlme package
library(nlme)
#apply the covid.arima.forecast function to the TSA.cumulative dataframe by TSA
#note that the output will be a list of dataframes
TSA.arima.output<-nlme::gapply(TSA.cumulative, FUN = covid.arima.forecast, groups=TSA.cumulative$Level_Name)

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#load data.table package
library(data.table)
TSA.arima.df<-data.table::rbindlist(TSA.arima.output, idcol=TRUE)


colnames(TSA.arima.df)[1] = 'Level_Name'
TSA.arima.df$Level = 'TSA'

#get Texas forecasts using covid.arima.forecast function
texas.arima.df<-covid.arima.forecast(state.cumulative)

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texas.arima.df$Level = 'State'
texas.arima.df$Level_Name = 'Texas'

#export arima results into .csv files
write.csv(texas.arima.df, 'statistical-output/time-series/df/state.csv', row.names = F)
write.csv(TSA.arima.df, 'statistical-output/time-series/df/tsa.csv', row.names = F)
write.csv(county.arima.df, 'statistical-output/time-series/df/county.csv', row.names = F)
write.csv(metro.arima.df, 'statistical-output/time-series/df/metro.csv', row.names = F)

STANDARD STATISTICAL TESTS

Ratio of cases

TSA level

library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:data.table':
## 
##     hour, isoweek, mday, minute, month, quarter, second, wday, week,
##     yday, year

## The following object is masked from 'package:base':
## 
##     date

cumcases.TSA <- TSA.cumulative %>% dplyr::select(Date,Level_Name, Cases_Cumulative)
cumcases.TSA$Date <- ymd(cumcases.TSA$Date)

latestdate <- max(cumcases.TSA$Date)
earliestdate <- latestdate - 14
middate <- latestdate-7

week2 <- subset(cumcases.TSA, Date==latestdate, select=Cases_Cumulative) - 
  subset(cumcases.TSA, Date==middate, select=Cases_Cumulative)

week1 <- subset(cumcases.TSA, Date==middate, select=Cases_Cumulative) - 
  subset(cumcases.TSA, Date==earliestdate, select=Cases_Cumulative)

current_ratio <- (week2/week1)[,1]
#current_ratio[week1<10] <- NA
current_ratio[current_ratio<0] <- NA
tsa_current_ratio_dat <- data.frame(tsa=subset(cumcases.TSA, Date==latestdate, select=Level_Name)[,1],
                                    current_ratio=current_ratio, current_ratio_cat = cut(current_ratio, breaks=c(0,0.5,.9,1.1,1.5,2,4,8,max(current_ratio))))

County level

cumcases.county <- county.cumulative %>% dplyr::select(Date,Level_Name, Cases_Cumulative)
cumcases.county$Date <- ymd(cumcases.county$Date)

latestdate <- max(cumcases.county$Date, na.rm=T)
earliestdate <- latestdate - 14
middate <- latestdate-7

week2 <- subset(cumcases.county, Date==latestdate, select=Cases_Cumulative) - 
  subset(cumcases.county, Date==middate, select=Cases_Cumulative)
week1 <- subset(cumcases.county, Date==middate, select=Cases_Cumulative) - 
  subset(cumcases.county, Date==earliestdate, select=Cases_Cumulative)

current_ratio <- (week2/week1)[,1]
current_ratio[week1<10] <- NA
current_ratio[current_ratio<0] <- NA
county_current_ratio_dat <- data.frame(county=subset(cumcases.county, Date==latestdate, select=Level_Name)[,1], current_ratio=current_ratio, current_ratio_cat = cut(current_ratio, breaks=c(0,0.5,.9,1.1,1.5,2,4,8,max(current_ratio))))

Metropolitan level

cumcases.metro <- metro.cumulative %>% dplyr::select(Date, Level_Name, Cases_Cumulative)
cumcases.metro$Date <- ymd(cumcases.metro$Date)

latestdate <- max(cumcases.metro$Date, na.rm=T)
earliestdate <- latestdate - 14
middate <- latestdate-7

week2 <- subset(cumcases.metro, Date==latestdate, select=Cases_Cumulative) - 
  subset(cumcases.metro, Date==middate, select=Cases_Cumulative)
week1 <- subset(cumcases.metro, Date==middate, select=Cases_Cumulative) - 
  subset(cumcases.metro, Date==earliestdate, select=Cases_Cumulative)

current_ratio <- (week2/week1)[,1]
current_ratio[current_ratio<0] <- NA
metro_current_ratio_dat <- data.frame(metro=subset(cumcases.metro, Date==latestdate, select=Level_Name)[,1], current_ratio=current_ratio, current_ratio_cat = cut(current_ratio, breaks=c(0,0.5,.9,1.1,1.5,2,4,8,max(current_ratio))))

Texas level

cumcases.state <- state.cumulative %>% dplyr::select(Date, Cases_Cumulative)
cumcases.state$Date <- ymd(cumcases.state$Date)

latestdate <- max(cumcases.state$Date, na.rm=T)
earliestdate <- latestdate - 14
middate <- latestdate-7

week2 <- subset(cumcases.state, Date==latestdate, select=Cases_Cumulative) - 
  subset(cumcases.state, Date==middate, select=Cases_Cumulative)
week1 <- subset(cumcases.state, Date==middate, select=Cases_Cumulative) - 
  subset(cumcases.state, Date==earliestdate, select=Cases_Cumulative)

current_ratio <- (week2/week1)[,1]
current_ratio[current_ratio<0] <- NA
state_current_ratio_dat <- data.frame(current_ratio=current_ratio, current_ratio_cat = cut(current_ratio, breaks=c(0,0.5,.9,1.1,1.5,2,4,8,max(current_ratio))))

export

#export arima results into .csv files
write.csv(state_current_ratio_dat, 'statistical-output/standard-stats/case-ratios/state_case_ratio.csv', row.names = F)
write.csv(tsa_current_ratio_dat,'statistical-output/standard-stats/case-ratios/tsa_case_ratio.csv', row.names = F)
write.csv(county_current_ratio_dat, 'statistical-output/standard-stats/case-ratios/county_case_ratio.csv', row.names = F)
write.csv(metro_current_ratio_dat,'statistical-output/standard-stats/case-ratios/metro_case_ratio.csv', row.names = F)

rolling average

TSA

tsa<-read.csv("combined-datasets/tsa.csv")
tsa2 = tsa %>% group_by(TSA) %>% mutate(Cases_Cumulative_Mean_7Day = rollmean(Cases_Cumulative, 7, fill = NA),
                                        Cases_Daily_Mean_7Day = rollmean(Cases_Daily, 7, fill = NA))

write.csv(tsa2, 'combined-datasets/tsa.csv', row.names = F)

County

county<-read.csv("combined-datasets/county.csv")
county2 = county %>% group_by(County) %>% mutate(Cases_Cumulative_Mean_7Day = rollmean(Cases_Cumulative, 7, fill = NA),
                                                 Cases_Daily_Mean_7Day = rollmean(Cases_Daily, 7, fill = NA))

write.csv(county2, 'combined-datasets/county.csv', row.names = F)

Metro

metro <-read.csv("combined-datasets/metro.csv")

metro2 = metro %>% group_by(Metro_Area) %>% mutate(Cases_Cumulative_Mean_7Day = rollmean(Cases_Cumulative, 7, fill = NA),
                                                   Cases_Daily_Mean_7Day = rollmean(Cases_Daily, 7, fill = NA))

write.csv(metro2, 'combined-datasets/metro.csv', row.names = F)

State

state<-xlsx::read.xlsx('combined-datasets/state.xlsx', sheetIndex=1)

state2 = state %>% mutate(Cases_Cumulative_Mean_7Day = rollmean(Cases_Cumulative, 7, fill = NA),
                          Cases_Daily_Mean_7Day = rollmean(Cases_Daily, 7, fill = NA))

xlsx::write.xlsx(state2, file="combined-datasets/state.xlsx", sheetName="longitudinal", row.names=FALSE)

COVID Statistical Analysis

Hongyin Lai, Yuan Li, Tara Prezioso, Alison Rector, Jeffrey Brennan, Swaminathan Kumar, Jose-Miguel Yamal

6/5/2020

RT ANALYSIS

TIME SERIES

STANDARD STATISTICAL TESTS

Ratio of cases

TSA level

County level

Metropolitan level

Texas level

export

rolling average

TSA

County

Metro

State

MACHINE LEARNING